A neural-network-based method for predicting protein stability changes upon single point mutations
A neural-network-based method for predicting protein stability changes upon single point mutations
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DOI：
10.1093/bioinformatics/bth928
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发表时间：
2004-08-04
期刊：
影响因子：
5.8
通讯作者：
Casadio, Rita
中科院分区：
文献类型：
--
作者：
Capriotti, Emidio;Fariselli, Piero;Casadio, Rita
动机：蛋白质设计的一个重要要求是能够预测蛋白质稳定性的变化。不同的方法来解决这个任务已经被描述和他们的性能测试，考虑到全球的预测和实验数据之间的线性相关性。既不是直接的统计评估其预测性能，也不是直接比较不同的方法是可能的。最近，已经产生了关于单点突变后蛋白质稳定性变化的热力学数据的重要数据库（ProTherm）。这使得机器学习技术的应用，预测自由能稳定性的变化突变后开始从蛋白质sequence.Results：在本文中，我们提出了一种基于神经网络的方法来预测，如果一个给定的突变增加或减少相对于本机结构的蛋白质热力学稳定性。使用由1615个突变组成的数据集，我们的预测器正确地分类了数据库中>80%的突变。在相同的任务和使用相同的数据，我们的预测器比网络上可用的其他方法表现得更好。此外，当我们的系统与基于能量的方法相结合时，联合预测准确度提高了90%，这表明它可以用于提高现有方法的性能，并通常用于改进蛋白质设计策略。
Motivation: One important requirement for protein design is to be able to predict changes of protein stability uponmutation. Different methods addressing this task have been described and their performance tested considering global linear correlation between predicted and experimental data. Neither is direct statistical evaluation of their prediction performance available, nor is a direct comparison among different approaches possible. Recently, a significant database of thermodynamic data on protein stability changes upon single point mutation has been generated (ProTherm). This allows the application of machine learning techniques to predicting free energy stability changes upon mutation starting from the protein sequence.Results: In this paper, we present a neural-network-based method to predict if a given mutation increases or decreases the protein thermodynamic stability with respect to the native structure. Using a dataset consisting of 1615 mutations, our predictor correctly classifies >80% of the mutations in the database. On the same task and using the same data, our predictor performs better than other methods available on the Web. Moreover, when our system is coupled with energy-based methods, the joint prediction accuracy increases up to 90%, suggesting that it can be used to increase also the performance of pre-existing methods, and generally to improve protein design strategies.